1. Field of the Invention
The present invention relates generally to electronic content. More particularly, the present invention relates to the near real-time analysis of dynamic social and sensor data to interpret user situation to better recommend electronic content.
2. Description of the Related Art
The amount of electronic content, especially online content, has undergone an explosion in recent years, mostly due to the rise in prevalence of the Internet. This explosion further exacerbates the frustrations of already overwhelmed consumers, since the majority of the content and information is irrelevant to a particular user at a particular moment. More than ever before, consumers are longing for a solution that would put the information they need or things they like at their fingertips when they need them. On the other hand, businesses have accumulated an astronomical amount of data detailing consumers' online activities. It is commonly recognized that mining the data to understand consumers' interests and sentiments can enable better targeted marketing and advertisement.
The main weakness of prior art mining techniques is their blindness about the user's situation. At a particular moment, what a user needs or likes and what is appropriate to suggest to the user can be highly variable based upon the user's situation. For example, even though a user may like good wine, suggesting going to a wine tasting nearby may not be a good idea when his friends with him at the time do not like wine. In another example, a user may enjoy going to theaters, visiting museums, and hiking, but which of these activities to suggest may depend greatly on the weather, the users mood, and on whether of her friends who share a same interest are available.
While improving recommendation is important on any computing platform, it is especially important for devices with small form factors such as mobile phones, due to small display areas, lower processing speed, and more limited user input.
Longer term interests of a user can be inferred based on a user's historical interests, physical behavior, virtual behavior (such as online shopping patterns, etc.) However, determining a user's current “situation” (in terms of his or her current activity, location, emotion, etc.) to infer his or her potential shorter term needs would be quite valuable.